Postgres is the fastest growing transactional database in the world, but it wasn't built for analytical queries: aggregations, joins across millions of rows, dashboard-powering scans. MotherDuck brings Postgres compatibility and sub-second queries to your data stack.
As analytical workloads grow, Postgres hits limits: slow dashboards, queries competing with production writes. MotherDuck is purpose-built for the analytical side — and the Postgres wire protocol means your existing tools connect without changes.
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| Designed for | Transactional workloads (OLTP): row-level reads, writes, updates, constraints. Analytics is possible but not the primary design. | Analytical workloads (OLAP): aggregations, scans, joins across large datasets. Sub-second performance on the queries that slow Postgres down. |
| Storage model | Row-oriented. Every analytical query reads entire rows, even if you only need two columns. | Columnar. Queries only read the columns they need — orders of magnitude less I/O for analytical workloads. |
| Postgres compatibility | It is Postgres. | Supports the Postgres wire protocol. Connect existing Postgres clients, BI tools, and applications without changes. |
| Scaling analytics | Vertical only. Analytical queries compete with production writes for CPU, memory, and I/O. | Hypertenancy: each user gets an isolated Duckling. Analytics never compete with your production database. |
| Cost at scale | Self-managed: you own the infrastructure, tuning, and scaling. Managed (RDS/Aurora): costs grow quickly with instance size. | Billed by the second, $0.60–$36/hr. Serverless — no instances to manage. Scales to zero when idle. |
| Maintenance for analytics | Indexes, partitioning, VACUUM, connection pooling, read replicas — all to make analytics tolerable on a transactional system. | Entirely managed — just choose your instance size. No indexes to tune, no read replicas to maintain. |
| Dual execution | No equivalent. | DuckDB-Wasm runs in the browser for ultra-fast in-browser analytics. Join local and cloud data in one query. |
| Local development | Run Postgres locally (different engine than managed services like Aurora). | DuckDB runs on your laptop — same SQL, same engine as cloud. Change one connection string to deploy. |
| AI integration | No native MCP or agent support. | Bring your own agent via MCP. Authenticate and start querying, no intermediary layer. |
| Native business intelligence | No native BI. Connect external tools. | Dives: agent-native data apps included. Create any experience in React + SQL, then deploy internally or embed. |
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Analytical queries that take minutes on Postgres finish in seconds on MotherDuck — and your production database stays untouched.
Purpose-built for analytics
Offload analytics, protect production
We're scaling rapidly, but our Postgres cluster kept falling over on analytical workloads, even after throwing hardware at it. With AI labs and Fortune 100 customers depending on us, that wasn't an option. Migrating to MotherDuck made our heaviest queries 30x faster, and it is now the source-of-truth for our core analytical workloads.
Ben Wiley
Co-Founder
The more I worked with it, the more I was just like, oh my God, this is so much simpler than working with Snowflake.
Pete Rafferty
Senior Software Engineer
Our data pipelines used to take eight hours. Now they're taking eight minutes, and I see a world where they take eight seconds.
Jim O'Neill
Co-Founder & CTO
PostgreSQL handles transactions brilliantly. MotherDuck handles analytics at scale. Use both: keep Postgres for your application data, use MotherDuck when queries need warehouse-speed and cloud compute.
PostgreSQL runs on a single server — heavy analytical queries degrade under concurrent load and lock rows that block transactions. Hypertenancy gives every user their own isolated Duckling with a vectorized DuckDB engine and 100ms cold start. Analytical workloads stay completely separated from your operational reads.
Scaling PostgreSQL for analytics means provisioning larger instances, read replicas, or a full migration — all with fixed costs and ops overhead. MotherDuck instances scale to zero between queries and bill by the second, so you pay for analytical work, not idle capacity sitting next to your Postgres box.
PostgreSQL has no native AI or MCP interface. MotherDuck connects to any AI agent via the MCP Server — query, explore, and manage your analytical data in natural language. The same SQL you know from Postgres, with the intelligence layer your data team actually wants to use.
Building analytics on PostgreSQL means wiring up a separate BI tool and hoping your Postgres instance handles concurrent reads. MotherDuck Dives are included — build any visualization or embedded analytics experience with an AI agent, backed by warehouse-scale compute rather than a transactional row store.
MotherDuck speaks the Postgres wire protocol natively. Your existing Postgres clients, ORMs, and third-party tools connect to MotherDuck without modification. You keep the ecosystem you know — just swap the connection string and get columnar, vectorized analytical speed.
ClickHouse is fast, but speed alone doesn't ship products. MergeTree tuning, shard balancing, non-standard SQL, and operational overhead add up. MotherDuck gives you sub-second analytics with zero infrastructure to manage.
Snowflake is a popular cloud data warehouse, but high costs and management complexity hold data teams back. Here's how MotherDuck and Snowflake compare.
Databricks is built for ML pipelines and large-scale Spark workloads. If your team's primary job is SQL analytics, MotherDuck is purpose-built for it — no JVM, no cluster tuning, no DBU math.
VACUUM, WLM queues, cluster resizing: Redshift is a full time job. MotherDuck is ultra fast and fully serverless — just sub-second analytics, billed by the second.
Surprise bills, shared-slot contention, partitioning just to control costs: BigQuery's pricing model punishes you for querying your own data. MotherDuck is flat per-second pricing — no scanning tax, no surprises.
Fly faster on MotherDuck, for internal insights or in your application.
